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About 6 years ago I invested in a ML as a startup company as an angel. The proposition was that they had a very nice workflow tool to take data, scrub it, parti

About 6 years ago I invested in a ML as a startup company as an angel. The proposition was that they had a very nice workflow tool to take data, scrub it, partion it, build models against it, view performance and deploy it through and api. At the time there was nothing else out there really that was as polished and understandable to a business person. Then, very rapidly, the field got democratized. People were firing up notebooks, tensor flow popped into existence and their edge eroded. They were pitching to teams who had just hired data scientists who of course didn't want to buy something that made their job look easier! The other reality, which ties to this article, is that whilst they had to do a lot of hand holding with clients so the service cost became high although this wasn't custom model development but more training on the platform so they eventually worked out a way to package it in the sale as they got established. The company in the end changed course. They built out the product to focus on a single vertical that was getting traction in finance. This was huge contentious change and they lost some people along the way - but - it ultimately saved them from being wiped out. In the end the sold out to a much larger player in that vertical who was had no AI strategy and we all made a decent if not mind blowing return on our investment. Lots of lessons learnt on the way and it was a fun ride overall.

dhruvkar 7y ago
was this evolution.ai?
scottlocklin 7y ago
Dunno if this is the startup I'm thinking of, but if it is, I'd attribute the exit entirely to the VC dude who took it over who obviously knew what he was doing. The founder-CEO was great at math, but it didn't look so good otherwise.
ubu7737 7y ago
Making money on the bubble.
svij137 7y ago
Great experience. Would like to understand more from your experience as we build our own venture in the super computing space for data scientists and AI workloads.
thaumasiotes 7y ago
> They were pitching to teams who had just hired data scientists who of course didn't want to buy something that made their job look easier! I don't really follow. Nobody thinks a guy driving a forklift is working just as hard as a guy lifting crates by hand. But he's doing more lifting! Why wouldn't you want your job to look easier? The easier it is, the more you can do.
- kelnage 7y ago
  To me, this sounds a lot like a justification built upon an bias (i.e. of course the tool the start-up was trying to sell was what the potential customers needed, it must have been those darn data scientists preventing the sales). From my experiences at the time, there are a lot of such start-ups around, most of which did not actually entirely (or often at all) meet the needs of their customers, and were promising a lot more than they actually delivered. Whereas having data scientists in your organisation meant that at the very least, they were able to develop a proper understanding of your business, and actually create real value.
- Loughla 7y ago
  >Why wouldn't you want your job to look easier? Automation has taught us that if your job looks easier, it takes substantially fewer of you to do it. Data scientists, in this instance, seem to be protecting themselves from what has happened in manufacturing for the last 50 years.
  - avip 7y ago
    Well we all work with data scientists and can firmly attest this is just false. They will work with whatever let's them focus on the actual domain problem, or a toolchain they are familiar with, or with something forced on them by org (while ranting). You know, exactly like developers
    - mint2 7y ago
      > or with something forced on them by org (while ranting). Too true.
Gravityloss 7y ago
Thank you, a very interesting story with a lot happening, told with only a few sentences.
bald 7y ago
> The company in the end changed course. They built out the product to focus on a single vertical that was getting traction in finance. Building a startup in the "AI" space myself since more than 2yrs now, I'm more convinced than ever that this one major elemnent of a successful strategy for such companies: focus on 1 vertical.
- monkeydust 7y ago
  100%. Faster your realise and act on it the quicker you will find your path in front of you get clearer and your chance of success will shoot up.
sandGorgon 7y ago
>The proposition was that they had a very nice workflow tool to take data, scrub it, partion it, build models against it, view performance question - what is the best tool in this exact space right now ? (minus deployment)
- tomrod 7y ago
  MLFlow

dhruvkar 7y ago

was this evolution.ai?

scottlocklin 7y ago

Dunno if this is the startup I'm thinking of, but if it is, I'd attribute the exit entirely to the VC dude who took it over who obviously knew what he was doing. The founder-CEO was great at math, but it didn't look so good otherwise.

ubu7737 7y ago

Making money on the bubble.

svij137 7y ago

Great experience. Would like to understand more from your experience as we build our own venture in the super computing space for data scientists and AI workloads.

thaumasiotes 7y ago

> They were pitching to teams who had just hired data scientists who of course didn't want to buy something that made their job look easier! I don't really follow. Nobody thinks a guy driving a forklift is working just as hard as a guy lifting crates by hand. But he's doing more lifting! Why wouldn't you want your job to look easier? The easier it is, the more you can do.

kelnage 7y ago
To me, this sounds a lot like a justification built upon an bias (i.e. of course the tool the start-up was trying to sell was what the potential customers needed, it must have been those darn data scientists preventing the sales). From my experiences at the time, there are a lot of such start-ups around, most of which did not actually entirely (or often at all) meet the needs of their customers, and were promising a lot more than they actually delivered. Whereas having data scientists in your organisation meant that at the very least, they were able to develop a proper understanding of your business, and actually create real value.
Loughla 7y ago
>Why wouldn't you want your job to look easier? Automation has taught us that if your job looks easier, it takes substantially fewer of you to do it. Data scientists, in this instance, seem to be protecting themselves from what has happened in manufacturing for the last 50 years.
- avip 7y ago
  Well we all work with data scientists and can firmly attest this is just false. They will work with whatever let's them focus on the actual domain problem, or a toolchain they are familiar with, or with something forced on them by org (while ranting). You know, exactly like developers
  - mint2 7y ago
    > or with something forced on them by org (while ranting). Too true.

Gravityloss 7y ago

Thank you, a very interesting story with a lot happening, told with only a few sentences.

bald 7y ago

> The company in the end changed course. They built out the product to focus on a single vertical that was getting traction in finance. Building a startup in the "AI" space myself since more than 2yrs now, I'm more convinced than ever that this one major elemnent of a successful strategy for such companies: focus on 1 vertical.

monkeydust 7y ago
100%. Faster your realise and act on it the quicker you will find your path in front of you get clearer and your chance of success will shoot up.

sandGorgon 7y ago

>The proposition was that they had a very nice workflow tool to take data, scrub it, partion it, build models against it, view performance question - what is the best tool in this exact space right now ? (minus deployment)

tomrod 7y ago
MLFlow